NAMIC Wiki - User contributions [en]

Projects:ModelingFunctionalActivationPatterns

2012-11-28T19:59:59Z

Georgehchen: /* Literature */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Modeling Functional Activation Patterns =

For a given cognitive task such as language processing, the location of
corresponding functional regions in the brain may vary across subjects
relative to anatomy. We present a probabilistic generative model that accounts
for such variability as observed in functional magnetic resonance imaging
(fMRI) data. We relate our approach to
sparse coding that estimates a basis consisting of functional regions in the
brain. Individual fMRI data is represented as a weighted sum of these
functional regions that undergo deformations. We demonstrate the proposed
method on a language fMRI study. Our method identified activation regions
that agree with known literature on language processing and established
correspondences among activation regions across subjects, producing more
robust group-level effects than anatomical alignment alone.

= Description =

We propose a novel way to characterize functional variability
that combines insights from prior work. We model
each subject's activation map as a weighted sum of group-level functional
activation parcels that undergo a subject-specific deformation. Similar to Xu
et al. [1], we define a hierarchical generative model,
but instead of using a Gaussian mixture model to represent shapes, we
represent each parcel as an image, which allows for complex shapes. By
representing each subject's activation in terms of group-level parcels, our
model maintains parcel correspondences across subjects, similar
to [2]. Next, we assume that the template regions can deform
to account for functional variability.
This involves using groupwise registration similar to [3]
that is guided by estimated group-level functional activation regions. We
perform inference within the proposed model using an algorithm similar to
expectation-maximization (EM) and illustrate our method on the
language system, which is known to have significant functional
variability [4].

== Experiments ==

We train our model on an fMRI study of 82 subjects reading sentences and
pronounceable non-words [4]. First, we apply the standard
fMRI general linear model for the sentences vs. non-words contrast.
We apply our model to subjects' t-statistic maps
thresholded at p-value=0.01. These images are pre-aligned using the subjects'
anatomical MRI scans via affine registration.

[[File:Georgehc disc group parcel supports.png|800px|thumb|center|
Estimated dictionary.
(a) Four slices showing the spatial support of the
extracted dictionary elements. Different colors correspond to
distinct dictionary elements where there is some overlap between
dictionary elements. From left to right: left frontal lobe and
temporal regions, medial prefrontal cortex and posterior
cingulate/precuneus, right cerebellum, and right temporal lobe.
Dictionary element indices correspond to those in Fig. 2.
(b) A single slice from three different estimated dictionary
elements where the dictionary element magnitude varies from low
(blue) to high (red).
From left to right: left posterior temporal lobe, left
anterior temporal lobe, left inferior frontal gyrus.]]

[[File:Georgehc disc wrfx.png|800px|thumb|center|
Top 25% of significance values (negative log p-values obtained
via weighted random effects analysis) within
dictionary element supports. For each dictionary element, "A"
refers to anatomical alignment, and "F" refers to alignment via
deformations learned by our method.]]

Fig. 1(a)
shows the spatial support of the final learned
dictionary elements on four slices.
Fig. 1(b) illustrates some
of the dictionary elements extracted by the algorithm. The dictionary elements
include regions previously reported as indicative of lexical and structural
language processing [4], namely portions of the temporal lobes,
the right cerebellum, and the left frontal lobe. There are also dictionary
elements corresponding to the medial prefrontal cortex, the posterior
cingulate, and the precuneus.

To evaluate the quality of the estimated alignment, we apply the estimated
deformation to held-out time course data for each subject and perform standard
weighted random effects analysis. We then look at significance
values within the support of each dictionary element. Importantly, for drawing
conclusions on the group-level parcels defined by the estimated dictionary
elements, within each parcel, it is the peak and regions around the peak that
are of interest rather than the full support of the dictionary element. Thus,
to quantify the advantage of our method, within each dictionary element, we
compare the top 25% highest significance values for our method versus those
of anatomical alignment (Fig. 2). We observe that accounting for
functional variability via deformations results in substantially higher peak
significance values within the estimated group-level parcels, suggesting
better overlap of these functional activation regions across subjects. On
average, our method improves the significance of group analysis by roughly an
order of magnitude when evaluating the top 25% significance values. Even if
we look at the top 50% of significance values in each dictionary element, the
results remain similar to those in Fig. 2.

== Conclusion ==

We developed a model that accounts for spatial variability of functional
activation regions in the brain via deformations of weighted dictionary
elements. Learning model parameters and estimating deformations yield
correspondences of functional activation regions in the brain across subjects.
We demonstrate our model in a language fMRI study, which contains substantial
variability. We plan to validate the detected parcels using data from
different fMRI language experiments.

== Literature ==

[1] L. Xu, T.D. Johnson, T.E. Nichols, and D.E. Nee. "Modeling inter-subject variability
in fMRI activation location: a bayesian hierarchical spatial model". Biometrics 65(4),
1041–1051, 2009.

[2] B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.F. Mangin, J.B. Poline.
"Structural analysis of fMRI data revisited: improving the sensitivity and
reliability of fMRI group studies". IEEE Transactions in Medical Imaging 26(9),
1256–1269, 2007.

[3] M.R. Sabuncu, B.D. Singer, B. Conroy, R.E. Bryan, P.J. Ramadge, J.V. Haxby.
"Function-based intersubject alignment of human cortical anatomy". Cerebral
Cortex 20(1), 130–140, 2010.

[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Castanon, S. Whitfield-Gabrieli, N. Kanwisher.
"New method for fMRI investigations of language: Defining ROIs functionally
in individual subjects". Neurophysiology 104, 1177–1194, 2010.

= Key Investigators =

MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3AModelingFunctionalActivationPatterns&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Modeling Functional Activation Patterns]

Projects:ModelingFunctionalActivationPatterns

2012-11-28T19:55:35Z

Georgehchen:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Modeling Functional Activation Patterns =

For a given cognitive task such as language processing, the location of
corresponding functional regions in the brain may vary across subjects
relative to anatomy. We present a probabilistic generative model that accounts
for such variability as observed in functional magnetic resonance imaging
(fMRI) data. We relate our approach to
sparse coding that estimates a basis consisting of functional regions in the
brain. Individual fMRI data is represented as a weighted sum of these
functional regions that undergo deformations. We demonstrate the proposed
method on a language fMRI study. Our method identified activation regions
that agree with known literature on language processing and established
correspondences among activation regions across subjects, producing more
robust group-level effects than anatomical alignment alone.

= Description =

We propose a novel way to characterize functional variability
that combines insights from prior work. We model
each subject's activation map as a weighted sum of group-level functional
activation parcels that undergo a subject-specific deformation. Similar to Xu
et al. [1], we define a hierarchical generative model,
but instead of using a Gaussian mixture model to represent shapes, we
represent each parcel as an image, which allows for complex shapes. By
representing each subject's activation in terms of group-level parcels, our
model maintains parcel correspondences across subjects, similar
to [2]. Next, we assume that the template regions can deform
to account for functional variability.
This involves using groupwise registration similar to [3]
that is guided by estimated group-level functional activation regions. We
perform inference within the proposed model using an algorithm similar to
expectation-maximization (EM) and illustrate our method on the
language system, which is known to have significant functional
variability [4].

== Experiments ==

We train our model on an fMRI study of 82 subjects reading sentences and
pronounceable non-words [4]. First, we apply the standard
fMRI general linear model for the sentences vs. non-words contrast.
We apply our model to subjects' t-statistic maps
thresholded at p-value=0.01. These images are pre-aligned using the subjects'
anatomical MRI scans via affine registration.

[[File:Georgehc disc group parcel supports.png|800px|thumb|center|
Estimated dictionary.
(a) Four slices showing the spatial support of the
extracted dictionary elements. Different colors correspond to
distinct dictionary elements where there is some overlap between
dictionary elements. From left to right: left frontal lobe and
temporal regions, medial prefrontal cortex and posterior
cingulate/precuneus, right cerebellum, and right temporal lobe.
Dictionary element indices correspond to those in Fig. 2.
(b) A single slice from three different estimated dictionary
elements where the dictionary element magnitude varies from low
(blue) to high (red).
From left to right: left posterior temporal lobe, left
anterior temporal lobe, left inferior frontal gyrus.]]

[[File:Georgehc disc wrfx.png|800px|thumb|center|
Top 25% of significance values (negative log p-values obtained
via weighted random effects analysis) within
dictionary element supports. For each dictionary element, "A"
refers to anatomical alignment, and "F" refers to alignment via
deformations learned by our method.]]

Fig. 1(a)
shows the spatial support of the final learned
dictionary elements on four slices.
Fig. 1(b) illustrates some
of the dictionary elements extracted by the algorithm. The dictionary elements
include regions previously reported as indicative of lexical and structural
language processing [4], namely portions of the temporal lobes,
the right cerebellum, and the left frontal lobe. There are also dictionary
elements corresponding to the medial prefrontal cortex, the posterior
cingulate, and the precuneus.

To evaluate the quality of the estimated alignment, we apply the estimated
deformation to held-out time course data for each subject and perform standard
weighted random effects analysis. We then look at significance
values within the support of each dictionary element. Importantly, for drawing
conclusions on the group-level parcels defined by the estimated dictionary
elements, within each parcel, it is the peak and regions around the peak that
are of interest rather than the full support of the dictionary element. Thus,
to quantify the advantage of our method, within each dictionary element, we
compare the top 25% highest significance values for our method versus those
of anatomical alignment (Fig. 2). We observe that accounting for
functional variability via deformations results in substantially higher peak
significance values within the estimated group-level parcels, suggesting
better overlap of these functional activation regions across subjects. On
average, our method improves the significance of group analysis by roughly an
order of magnitude when evaluating the top 25% significance values. Even if
we look at the top 50% of significance values in each dictionary element, the
results remain similar to those in Fig. 2.

== Conclusion ==

We developed a model that accounts for spatial variability of functional
activation regions in the brain via deformations of weighted dictionary
elements. Learning model parameters and estimating deformations yield
correspondences of functional activation regions in the brain across subjects.
We demonstrate our model in a language fMRI study, which contains substantial
variability. We plan to validate the detected parcels using data from
different fMRI language experiments.

== Literature ==

[1] L. Xu, T.D. Johnson, T.E. Nichols, and D.E. Nee. "Modeling inter-subject variability
in fMRI activation location: a bayesian hierarchical spatial model". Biometrics 65(4),
1041–1051, 2009.

[2] B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.F. Mangin, J.B. Poline.
"Structural analysis of fMRI data revisited: improving the sensitivity and
reliability of fMRI group studies". IEEE Transactions in Medical Imaging 26(9),
1256–1269, 2007.

[3] M.R. Sabuncu, B.D. Singer, B. Conroy, R.E. Bryan, P.J. Ramadge, J.V. Haxby.
"Function-based intersubject alignment of human cortical anatomy". Cerebral
Cortex 20(1), 130–140, 2010.

[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Casta ̃on, S. Whitfield-Gabrieli, N. Kanwisher.
"New method for fMRI investigations of language: Defining ROIs functionally
in individual subjects". Neurophysiology 104, 1177–1194, 2010.

= Key Investigators =

MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3AModelingFunctionalActivationPatterns&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Modeling Functional Activation Patterns]

Projects:ModelingFunctionalActivationPatterns

2012-11-28T19:53:51Z

Georgehchen:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Modeling Functional Activation Patterns =

For a given cognitive task such as language processing, the location of
corresponding functional regions in the brain may vary across subjects
relative to anatomy. We present a probabilistic generative model that accounts
for such variability as observed in functional magnetic resonance imaging
(fMRI) data. We relate our approach to
sparse coding that estimates a basis consisting of functional regions in the
brain. Individual fMRI data is represented as a weighted sum of these
functional regions that undergo deformations. We demonstrate the proposed
method on a language fMRI study. Our method identified activation regions
that agree with known literature on language processing and established
correspondences among activation regions across subjects, producing more
robust group-level effects than anatomical alignment alone.

= Description =

We propose a novel way to characterize functional variability
that combines insights from prior work. We model
each subject's activation map as a weighted sum of group-level functional
activation parcels that undergo a subject-specific deformation. Similar to Xu
et al. [1], we define a hierarchical generative model,
but instead of using a Gaussian mixture model to represent shapes, we
represent each parcel as an image, which allows for complex shapes. By
representing each subject's activation in terms of group-level parcels, our
model maintains parcel correspondences across subjects, similar
to [2]. Next, we assume that the template regions can deform
to account for functional variability.
This involves using groupwise registration similar to [3]
that is guided by estimated group-level functional activation regions. We
perform inference within the proposed model using an algorithm similar to
expectation-maximization (EM) and illustrate our method on the
language system, which is known to have significant functional
variability [4].

== Experiments ==

We train our model on an fMRI study of 82 subjects reading sentences and
pronounceable non-words [4]. First, we apply the standard
fMRI general linear model for the sentences vs. non-words contrast.
We apply our model to subjects' t-statistic maps
thresholded at p-value=0.01. These images are pre-aligned using the subjects'
anatomical MRI scans via affine registration.

[[File:Georgehc disc group parcel supports.png|800px|thumb|center|
Estimated dictionary.
(a) Four slices showing the spatial support of the
extracted dictionary elements. Different colors correspond to
distinct dictionary elements where there is some overlap between
dictionary elements. From left to right: left frontal lobe and
temporal regions, medial prefrontal cortex and posterior
cingulate/precuneus, right cerebellum, and right temporal lobe.
Dictionary element indices correspond to those in Fig. 2.
(b) A single slice from three different estimated dictionary
elements where the dictionary element magnitude varies from low
(blue) to high (red).
From left to right: left posterior temporal lobe, left
anterior temporal lobe, left inferior frontal gyrus.]]

[[File:Georgehc disc wrfx.png|800px|thumb|center|
Top 25% of significance values (negative log p-values obtained
via weighted random effects analysis) within
dictionary element supports. For each dictionary element, "A"
refers to anatomical alignment, and "F" refers to alignment via
deformations learned by our method.]]

Fig. 1(a)
shows the spatial support of the final learned
dictionary elements on four slices.
Fig. 1(b) illustrates some
of the dictionary elements extracted by the algorithm. The dictionary elements
include regions previously reported as indicative of lexical and structural
language processing [4], namely portions of the temporal lobes,
the right cerebellum, and the left frontal lobe. There are also dictionary
elements corresponding to the medial prefrontal cortex, the posterior
cingulate, and the precuneus.

To evaluate the quality of the estimated alignment, we apply the estimated
deformation to held-out time course data for each subject and perform standard
weighted random effects analysis. We then look at significance
values within the support of each dictionary element. Importantly, for drawing
conclusions on the group-level parcels defined by the estimated dictionary
elements, within each parcel, it is the peak and regions around the peak that
are of interest rather than the full support of the dictionary element. Thus,
to quantify the advantage of our method, within each dictionary element, we
compare the top 25% highest significance values for our method versus those
of anatomical alignment (Fig. 2). We observe that accounting for
functional variability via deformations results in substantially higher peak
significance values within the estimated group-level parcels, suggesting
better overlap of these functional activation regions across subjects. On
average, our method improves the significance of group analysis by roughly an
order of magnitude when evaluating the top 25% significance values. Even if
we look at the top 50% of significance values in each dictionary element, the
results remain similar to those in Fig. 2.

== Conclusion ==

We developed a model that accounts for spatial variability of functional
activation regions in the brain via deformations of weighted dictionary
elements. Learning model parameters and estimating deformations yield
correspondences of functional activation regions in the brain across subjects.
We demonstrate our model in a language fMRI study, which contains substantial
variability. We plan to validate the detected parcels using data from
different fMRI language experiments.

== Literature ==

[1] L. Xu, T.D. Johnson, T.E. Nichols, and D.E. Nee. "Modeling inter-subject variability
in fMRI activation location: a bayesian hierarchical spatial model". Biometrics 65(4),
1041–1051, 2009.

[2] B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.F. Mangin, J.B. Poline.
"Structural analysis of fMRI data revisited: improving the sensitivity and
reliability of fMRI group studies". IEEE Transactions in Medical Imaging 26(9),
1256–1269, 2007.

[3] M.R. Sabuncu, B.D. Singer, B. Conroy, R.E. Bryan, P.J. Ramadge, J.V. Haxby.
"Function-based intersubject alignment of human cortical anatomy". Cerebral
Cortex 20(1), 130–140, 2010.

[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Casta ̃on, S. Whitfield-Gabrieli, N. Kanwisher.
"New method for fMRI investigations of language: Defining ROIs functionally
in individual subjects". Neurophysiology 104, 1177–1194, 2010.

= Key Investigators =

MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland

= Publications =

Projects:ModelingFunctionalActivationPatterns

2012-11-28T19:51:44Z

Georgehchen:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Modeling Functional Activation Patterns =

For a given cognitive task such as language processing, the location of
corresponding functional regions in the brain may vary across subjects
relative to anatomy. We present a probabilistic generative model that accounts
for such variability as observed in functional magnetic resonance imaging
(fMRI) data. We relate our approach to
sparse coding that estimates a basis consisting of functional regions in the
brain. Individual fMRI data is represented as a weighted sum of these
functional regions that undergo deformations. We demonstrate the proposed
method on a language fMRI study. Our method identified activation regions
that agree with known literature on language processing and established
correspondences among activation regions across subjects, producing more
robust group-level effects than anatomical alignment alone.

= Description =

We propose a novel way to characterize functional variability
that combines insights from prior work. We model
each subject's activation map as a weighted sum of group-level functional
activation parcels that undergo a subject-specific deformation. Similar to Xu
et al. [1], we define a hierarchical generative model,
but instead of using a Gaussian mixture model to represent shapes, we
represent each parcel as an image, which allows for complex shapes. By
representing each subject's activation in terms of group-level parcels, our
model maintains parcel correspondences across subjects, similar
to [2]. Next, we assume that the template regions can deform
to account for functional variability.
This involves using groupwise registration similar to [3]
that is guided by estimated group-level functional activation regions. We
perform inference within the proposed model using an algorithm similar to
expectation-maximization (EM) and illustrate our method on the
language system, which is known to have significant functional
variability [4].

== Experiments ==

We train our model on an fMRI study of 82 subjects reading sentences and
pronounceable non-words [4]. First, we apply the standard
fMRI general linear model for the sentences vs.~non-words contrast.
We apply our model to subjects' t-statistic maps
thresholded at p-value=0.01. These images are pre-aligned using the subjects'
anatomical MRI scans via affine registration.

[[File:Georgehc disc group parcel supports.png|800px|thumb|center|]]

[[File:Georgehc disc wrfx.png|800px|thumb|center|]]

Fig. 1(a)
shows the spatial support of the final learned
dictionary elements on four slices.
Fig. 1(b) illustrates some
of the dictionary elements extracted by the algorithm. The dictionary elements
include regions previously reported as indicative of lexical and structural
language processing [4], namely portions of the temporal lobes,
the right cerebellum, and the left frontal lobe. There are also dictionary
elements corresponding to the medial prefrontal cortex, the posterior
cingulate, and the precuneus.

To evaluate the quality of the estimated alignment, we apply the estimated
deformation to held-out time course data for each subject and perform standard
weighted random effects analysis. We then look at significance
values within the support of each dictionary element. Importantly, for drawing
conclusions on the group-level parcels defined by the estimated dictionary
elements, within each parcel, it is the peak and regions around the peak that
are of interest rather than the full support of the dictionary element. Thus,
to quantify the advantage of our method, within each dictionary element, we
compare the top 25% highest significance values for our method versus those
of anatomical alignment (Fig. 2). We observe that accounting for
functional variability via deformations results in substantially higher peak
significance values within the estimated group-level parcels, suggesting
better overlap of these functional activation regions across subjects. On
average, our method improves the significance of group analysis by roughly an
order of magnitude when evaluating the top 25% significance values. Even if
we look at the top 50% of significance values in each dictionary element, the
results remain similar to those in Fig. 2.

== Conclusion ==

We developed a model that accounts for spatial variability of functional
activation regions in the brain via deformations of weighted dictionary
elements. Learning model parameters and estimating deformations yield
correspondences of functional activation regions in the brain across subjects.
We demonstrate our model in a language fMRI study, which contains substantial
variability. We plan to validate the detected parcels using data from
different fMRI language experiments.

== Literature ==

[1] L. Xu, T.D. Johnson, T.E. Nichols, and D.E. Nee. "Modeling inter-subject variability
in fMRI activation location: a bayesian hierarchical spatial model". Biometrics 65(4),
1041–1051, 2009.

[2] B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.F. Mangin, J.B. Poline.
"Structural analysis of fMRI data revisited: improving the sensitivity and
reliability of fMRI group studies". IEEE Transactions in Medical Imaging 26(9),
1256–1269, 2007.

[3] M.R. Sabuncu, B.D. Singer, B. Conroy, R.E. Bryan, P.J. Ramadge, J.V. Haxby.
"Function-based intersubject alignment of human cortical anatomy". Cerebral
Cortex 20(1), 130–140, 2010.

[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Casta ̃on, S. Whitfield-Gabrieli, N. Kanwisher.
"New method for fMRI investigations of language: Defining ROIs functionally
in individual subjects". Neurophysiology 104, 1177–1194, 2010.

= Key Investigators =

MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland

= Publications =

Projects:ModelingFunctionalActivationPatterns

2012-11-28T19:51:33Z

Georgehchen:

[[File:Example.jpg]] Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Modeling Functional Activation Patterns =

For a given cognitive task such as language processing, the location of
corresponding functional regions in the brain may vary across subjects
relative to anatomy. We present a probabilistic generative model that accounts
for such variability as observed in functional magnetic resonance imaging
(fMRI) data. We relate our approach to
sparse coding that estimates a basis consisting of functional regions in the
brain. Individual fMRI data is represented as a weighted sum of these
functional regions that undergo deformations. We demonstrate the proposed
method on a language fMRI study. Our method identified activation regions
that agree with known literature on language processing and established
correspondences among activation regions across subjects, producing more
robust group-level effects than anatomical alignment alone.

= Description =

We propose a novel way to characterize functional variability
that combines insights from prior work. We model
each subject's activation map as a weighted sum of group-level functional
activation parcels that undergo a subject-specific deformation. Similar to Xu
et al. [1], we define a hierarchical generative model,
but instead of using a Gaussian mixture model to represent shapes, we
represent each parcel as an image, which allows for complex shapes. By
representing each subject's activation in terms of group-level parcels, our
model maintains parcel correspondences across subjects, similar
to [2]. Next, we assume that the template regions can deform
to account for functional variability.
This involves using groupwise registration similar to [3]
that is guided by estimated group-level functional activation regions. We
perform inference within the proposed model using an algorithm similar to
expectation-maximization (EM) and illustrate our method on the
language system, which is known to have significant functional
variability [4].

== Experiments ==

We train our model on an fMRI study of 82 subjects reading sentences and
pronounceable non-words [4]. First, we apply the standard
fMRI general linear model for the sentences vs.~non-words contrast.
We apply our model to subjects' t-statistic maps
thresholded at p-value=0.01. These images are pre-aligned using the subjects'
anatomical MRI scans via affine registration.

[[File:Georgehc disc group parcel supports.png|800px|thumb|center|]]

[[File:Georgehc disc wrfx.png|800px|thumb|center|]]

Fig. 1(a)
shows the spatial support of the final learned
dictionary elements on four slices.
Fig. 1(b) illustrates some
of the dictionary elements extracted by the algorithm. The dictionary elements
include regions previously reported as indicative of lexical and structural
language processing [4], namely portions of the temporal lobes,
the right cerebellum, and the left frontal lobe. There are also dictionary
elements corresponding to the medial prefrontal cortex, the posterior
cingulate, and the precuneus.

To evaluate the quality of the estimated alignment, we apply the estimated
deformation to held-out time course data for each subject and perform standard
weighted random effects analysis. We then look at significance
values within the support of each dictionary element. Importantly, for drawing
conclusions on the group-level parcels defined by the estimated dictionary
elements, within each parcel, it is the peak and regions around the peak that
are of interest rather than the full support of the dictionary element. Thus,
to quantify the advantage of our method, within each dictionary element, we
compare the top 25% highest significance values for our method versus those
of anatomical alignment (Fig. 2). We observe that accounting for
functional variability via deformations results in substantially higher peak
significance values within the estimated group-level parcels, suggesting
better overlap of these functional activation regions across subjects. On
average, our method improves the significance of group analysis by roughly an
order of magnitude when evaluating the top 25% significance values. Even if
we look at the top 50% of significance values in each dictionary element, the
results remain similar to those in Fig. 2.

== Conclusion ==

We developed a model that accounts for spatial variability of functional
activation regions in the brain via deformations of weighted dictionary
elements. Learning model parameters and estimating deformations yield
correspondences of functional activation regions in the brain across subjects.
We demonstrate our model in a language fMRI study, which contains substantial
variability. We plan to validate the detected parcels using data from
different fMRI language experiments.

== Literature ==

[1] L. Xu, T.D. Johnson, T.E. Nichols, and D.E. Nee. "Modeling inter-subject variability
in fMRI activation location: a bayesian hierarchical spatial model". Biometrics 65(4),
1041–1051, 2009.

[2] B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.F. Mangin, J.B. Poline.
"Structural analysis of fMRI data revisited: improving the sensitivity and
reliability of fMRI group studies". IEEE Transactions in Medical Imaging 26(9),
1256–1269, 2007.

[3] M.R. Sabuncu, B.D. Singer, B. Conroy, R.E. Bryan, P.J. Ramadge, J.V. Haxby.
"Function-based intersubject alignment of human cortical anatomy". Cerebral
Cortex 20(1), 130–140, 2010.

[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Casta ̃on, S. Whitfield-Gabrieli, N. Kanwisher.
"New method for fMRI investigations of language: Defining ROIs functionally
in individual subjects". Neurophysiology 104, 1177–1194, 2010.

= Key Investigators =

MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland

= Publications =

File:Georgehc disc wrfx.png

2012-11-28T19:50:08Z

Georgehchen:

File:Georgehc disc group parcel supports.png

2012-11-28T19:49:52Z

Georgehchen:

Projects:ModelingFunctionalActivationPatterns

2012-11-28T19:48:46Z

Georgehchen:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Modeling Functional Activation Patterns =

For a given cognitive task such as language processing, the location of
corresponding functional regions in the brain may vary across subjects
relative to anatomy. We present a probabilistic generative model that accounts
for such variability as observed in functional magnetic resonance imaging
(fMRI) data. We relate our approach to
sparse coding that estimates a basis consisting of functional regions in the
brain. Individual fMRI data is represented as a weighted sum of these
functional regions that undergo deformations. We demonstrate the proposed
method on a language fMRI study. Our method identified activation regions
that agree with known literature on language processing and established
correspondences among activation regions across subjects, producing more
robust group-level effects than anatomical alignment alone.

= Description =

We propose a novel way to characterize functional variability
that combines insights from prior work. We model
each subject's activation map as a weighted sum of group-level functional
activation parcels that undergo a subject-specific deformation. Similar to Xu
et al. [1], we define a hierarchical generative model,
but instead of using a Gaussian mixture model to represent shapes, we
represent each parcel as an image, which allows for complex shapes. By
representing each subject's activation in terms of group-level parcels, our
model maintains parcel correspondences across subjects, similar
to [2]. Next, we assume that the template regions can deform
to account for functional variability.
This involves using groupwise registration similar to [3]
that is guided by estimated group-level functional activation regions. We
perform inference within the proposed model using an algorithm similar to
expectation-maximization (EM) and illustrate our method on the
language system, which is known to have significant functional
variability [4].

== Experiments ==

We train our model on an fMRI study of 82 subjects reading sentences and
pronounceable non-words [4]. First, we apply the standard
fMRI general linear model for the sentences vs.~non-words contrast.
We apply our model to subjects' t-statistic maps
thresholded at p-value=0.01. These images are pre-aligned using the subjects'
anatomical MRI scans via affine registration.

Fig. 1(a)
shows the spatial support of the final learned
dictionary elements on four slices.
Fig. 1(b) illustrates some
of the dictionary elements extracted by the algorithm. The dictionary elements
include regions previously reported as indicative of lexical and structural
language processing [4], namely portions of the temporal lobes,
the right cerebellum, and the left frontal lobe. There are also dictionary
elements corresponding to the medial prefrontal cortex, the posterior
cingulate, and the precuneus.

To evaluate the quality of the estimated alignment, we apply the estimated
deformation to held-out time course data for each subject and perform standard
weighted random effects analysis. We then look at significance
values within the support of each dictionary element. Importantly, for drawing
conclusions on the group-level parcels defined by the estimated dictionary
elements, within each parcel, it is the peak and regions around the peak that
are of interest rather than the full support of the dictionary element. Thus,
to quantify the advantage of our method, within each dictionary element, we
compare the top 25% highest significance values for our method versus those
of anatomical alignment (Fig. 2). We observe that accounting for
functional variability via deformations results in substantially higher peak
significance values within the estimated group-level parcels, suggesting
better overlap of these functional activation regions across subjects. On
average, our method improves the significance of group analysis by roughly an
order of magnitude when evaluating the top 25% significance values. Even if
we look at the top 50% of significance values in each dictionary element, the
results remain similar to those in Fig. 2.

== Conclusion ==

We developed a model that accounts for spatial variability of functional
activation regions in the brain via deformations of weighted dictionary
elements. Learning model parameters and estimating deformations yield
correspondences of functional activation regions in the brain across subjects.
We demonstrate our model in a language fMRI study, which contains substantial
variability. We plan to validate the detected parcels using data from
different fMRI language experiments.

== Literature ==

[1] L. Xu, T.D. Johnson, T.E. Nichols, and D.E. Nee. "Modeling inter-subject variability
in fMRI activation location: a bayesian hierarchical spatial model". Biometrics 65(4),
1041–1051, 2009.

[2] B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.F. Mangin, J.B. Poline.
"Structural analysis of fMRI data revisited: improving the sensitivity and
reliability of fMRI group studies". IEEE Transactions in Medical Imaging 26(9),
1256–1269, 2007.

[3] M.R. Sabuncu, B.D. Singer, B. Conroy, R.E. Bryan, P.J. Ramadge, J.V. Haxby.
"Function-based intersubject alignment of human cortical anatomy". Cerebral
Cortex 20(1), 130–140, 2010.

[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Casta ̃on, S. Whitfield-Gabrieli, N. Kanwisher.
"New method for fMRI investigations of language: Defining ROIs functionally
in individual subjects". Neurophysiology 104, 1177–1194, 2010.

= Key Investigators =

MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland

= Publications =

Projects:ModelingFunctionalActivationPatterns

2012-11-28T19:38:35Z

Georgehchen:

Projects:ModelingFunctionalActivationPatterns

2012-11-28T19:27:27Z

Georgehchen: Created page with ' Back to NA-MIC Collaborations, MIT Algorithms, __NOTOC__ = Modeling Functional Activation Patterns =…'

Algorithm:MIT

2011-10-28T18:20:31Z

Georgehchen: /* Quantitative Susceptibility Mapping */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal. [[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. In Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, B.H. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, 56(2):497-507, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

'''New: ''' A. Dalca, G. Danagoulian, R. Kikinis, E. Schmidt, and P. Golland. Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6893:537, 2011.

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:51:31Z

Georgehchen:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, B.H. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, 56(2):497-507, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

'''New: ''' A. Dalca, G. Danagoulian, R. Kikinis, E. Schmidt, and P. Golland. Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6893:537, 2011.

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:44:07Z

Georgehchen:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:43:17Z

Georgehchen:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:40:25Z

Georgehchen:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:31:17Z

Georgehchen: /* Nonparametric Models for Supervised Image Segmentation */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:31:05Z

Georgehchen: /* Joint Segmentation of Image Ensembles via Latent Atlases */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:30:46Z

Georgehchen: /* Brain Tumor Segmentation and Modeling */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:30:29Z

Georgehchen: /* Brain Connectivity */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

'''New: ''' B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:29:40Z

Georgehchen: /* Multi-variate activation detection */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

'''New: ''' B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

'''New: ''' A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

'''New: ''' A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:28:52Z

Georgehchen: /* Learning Task-Optimal Registration Cost Functions */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

'''New: ''' B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

'''New: ''' A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

'''New: ''' A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:28:30Z

Georgehchen: /* Improving fMRI Analysis using Supervised and Unsupervised Learning */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

'''New: ''' B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

'''New: ''' A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

'''New: ''' A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

'''New: ''' B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:28:01Z

Georgehchen: /* fMRI Detection and Analysis */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

'''New: ''' B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

'''New: ''' A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

'''New: ''' A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

'''New: ''' B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

'''New: ''' D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

'''New: ''' D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

'''New: ''' D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:27:42Z

Georgehchen: /* Multi-variate activation detection */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

'''New: ''' B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

'''New: ''' A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

'''New: ''' A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

'''New: ''' B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

'''New: ''' D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

'''New: ''' D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

'''New: ''' D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:FMRIEvaluationchart.jpg|200px]]
| |

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

'''New: ''' W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

| | [[Image:epi_correction_small.jpg|200px]]
| |

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
| |

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

| | [[Image:JointRegSeg.png|200px]]
| |

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

| | [[Image:Models.jpg|200px]]
| |

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

| | [[Image:brain.png|200px]]
| |

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

| | [[Image:Thalamus_algo_outline.png|200px]]
| |

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

| | [[Image:ConnectivityMap.png|200px]]
| |

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:27:15Z

Georgehchen: /* Spherical Demons: Fast Surface Registration */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

{| cellpadding="10" style="text-align:left;"

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

'''New: ''' M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

'''New: ''' M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

'''New: ''' T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

'''New: ''' B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

'''New: ''' B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

'''New: ''' A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

'''New: ''' A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

'''New: ''' Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

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| | [[Image:Atlas_OneCluster.png|center| 200px]]
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== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

'''New: ''' G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

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{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
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== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

'''New: ''' B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

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| | [[Image:CoordinateChart.png|250px]]
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== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

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| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
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== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

'''New: ''' D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

'''New: ''' D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

'''New: ''' D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

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| | [[Image:FMRIEvaluationchart.jpg|200px]]
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== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

'''New: ''' W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

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| | [[Image:GiniContrast_Icon.png|center| 150px]]
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== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

'''New: ''' G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

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| | [[Image:epi_correction_small.jpg|200px]]
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== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

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{| cellpadding="10" style="text-align:left;"
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== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

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| | [[Image:ICluster_templates.gif|250px]]
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== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

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| | [[Image:GroupwiseSummary.PNG|200px]]
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== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

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| | [[Image:JointRegSeg.png|200px]]
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== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

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| | [[Image:FoldingSpeedDetection.png|150px|]]
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== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

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| | [[Image:Models.jpg|200px]]
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== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

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| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
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== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

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| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
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== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

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| | [[Image:brain.png|200px]]
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== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

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| | [[Image:Thalamus_algo_outline.png|200px]]
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== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

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| | [[Image:ConnectivityMap.png|200px]]
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== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

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| | [[Image:HippocampalShapeDifferences.gif|200px]]
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== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

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Projects:NerveSegmentation

2011-03-31T21:41:02Z

Georgehchen: /* Results */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Nerve Segmentation =

Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. We present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bezier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking when compared to expert manual segmentation.

= Data =

[[File:NerveSegMREg.png|800px|thumb|center|We show three slices of an example High-Resolution MR scan. All arrows point to the same nerve bundle. Blue arrows show examples of poor contrast between the nerve and the surrounding tissue; orange arrows indicate the thickening of the neural tract into a ganglion.
]]

= Description =
We present a tracking approach based on particle filtering, also known as sequential Monte Carlo tracking. Tracking has also been used successfully for segmentation of tubular structures. Most vessel tracking methods model the state as a cross-sectional ellipse or as a cylindroid. In tracking nerve bundles, the regions of low contrast require the state to capture substantially longer segments of the track than what is represented by a cross-section. In addition, nerves tend to change direction, often sharply, which necessitates a use of more complex descriptors than cylinders.

To address the challenges of nerve tracking, we define a flexible particle representation that captures the geometric behavior of the nerve bundles. We use a Bezier spline centerline with a linear radius function to characterize a nerve bundle. We devise a dynamics model for particle updates that encourages continuity and smoothness. Furthermore, we define an image likelihood model that compares gradient fields and intensities of predicted patches with image observations to evaluate a posterior distribution of the particles' importance. Once tracking is completed, we remove spurious segmentations by measuring the quality of the entire tract. We demonstrate successful segmentations of neural tracts and evaluate them relative to expert manual segmentations. To the best of our knowledge, this is the first automatic segmentation of nerve bundles and ganglia.

= Results =

[[File:NerveSegResults.png|800px|thumb|center|
The above figure shows a summary of our results. On the left, we show a rendering of segmentation results -- the rightmost nerve shows results without post-processing pruning, while the left segmentation was processed after completion of tracking with an automatic post-processing step. On the right, we present two measures, precision and sensitivity, as box plots.]]

Here we compare our segmentations with expert delineations. We define precision as the percentage of voxels identified by the algorithm as nerve that were also marked as nerve by an expert. Precision coarsely quantifies our ability to separate the nerve from the surroundings. We also compute precision for a dilation of the manual segmentation by two and three voxels. The results for precision are shown in the left part of the graph, where we note that we reach a median 97.3% of our proposed segmentation being within 2 voxels of the expert segmentation, while due to partial volume effect we over-segment the thin nerves and we only achieve a 68% precision with respect to exact segmentation.

Since the main goal of nerve tracking is to extract the full path of the nerve accurately, while it is less crucial to identify the bundle boundaries, we also define sensitivity to quantify the agreement between the automatically defined path and the centerline of the manual segmentation. We define sensitivity as the percentage of voxels in the centerline of the expert segmentation that were also correctly identified by our algorithm. We also compute the same percentage for a dilation of the automatic segmentation, by two and three voxels. We achieve a media 96% of the centerline voxels being within 3 voxels of a prediction, while we find a drop to 53% when comparing with the exact centerline. The latter drop is due to segmentations in the areas of the thick ganglia, where our method under-segments, and may follow a slightly off-center path.

= Conclusion =

As shown in the results, the proposed segmentation may slightly over-segment (usually by at most two voxels) in thin areas and under-segment in thick areas, but will give a very good estimation of the nerve core and location.

==Key Investigators==
* MIT: Adrian Dalca, Polina Golland
* BWH: Giovanna Danagoulian, Ehud Schmidt
* SPL: Ron Kikinis

Projects:NerveSegmentation

2011-03-31T21:40:31Z

Georgehchen: /* Nerve Segmentation */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Nerve Segmentation =

Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. We present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bezier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking when compared to expert manual segmentation.

= Data =

[[File:NerveSegMREg.png|800px|thumb|center|We show three slices of an example High-Resolution MR scan. All arrows point to the same nerve bundle. Blue arrows show examples of poor contrast between the nerve and the surrounding tissue; orange arrows indicate the thickening of the neural tract into a ganglion.
]]

= Description =
We present a tracking approach based on particle filtering, also known as sequential Monte Carlo tracking. Tracking has also been used successfully for segmentation of tubular structures. Most vessel tracking methods model the state as a cross-sectional ellipse or as a cylindroid. In tracking nerve bundles, the regions of low contrast require the state to capture substantially longer segments of the track than what is represented by a cross-section. In addition, nerves tend to change direction, often sharply, which necessitates a use of more complex descriptors than cylinders.

To address the challenges of nerve tracking, we define a flexible particle representation that captures the geometric behavior of the nerve bundles. We use a Bezier spline centerline with a linear radius function to characterize a nerve bundle. We devise a dynamics model for particle updates that encourages continuity and smoothness. Furthermore, we define an image likelihood model that compares gradient fields and intensities of predicted patches with image observations to evaluate a posterior distribution of the particles' importance. Once tracking is completed, we remove spurious segmentations by measuring the quality of the entire tract. We demonstrate successful segmentations of neural tracts and evaluate them relative to expert manual segmentations. To the best of our knowledge, this is the first automatic segmentation of nerve bundles and ganglia.

= Results =

[[File:NerveSegResults.png|800px|thumb|center|
The above figure shows a summary of our results. On the left, we show a rendering of segmentation results -- the rightmost nerve shows results without post-processing pruning, while the left segmentation was processed after completion of tracking with an automatic post-processing step. On the right, we present two measures, precision and sensitivity, as box plots.]]

Here we compare our segmentations with expert delineations. We define precision as the percentage of voxels identified by the algorithm as nerve that were also marked as nerve by an expert. Precision coarsely quantifies our ability to separate the nerve from the surroundings. We also compute precision for a dilation of the manual segmentation by two and three voxels. The results for precision are shown in the left part of the graph, where we note that we reach a median $97.3\%$ of our proposed segmentation being within 2 voxels of the expert segmentation, while due to partial volume effect we over-segment the thin nerves and we only achieve a 68% precision with respect to exact segmentation.

Since the main goal of nerve tracking is to extract the full path of the nerve accurately, while it is less crucial to identify the bundle boundaries, we also define sensitivity to quantify the agreement between the automatically defined path and the centerline of the manual segmentation. We define sensitivity as the percentage of voxels in the centerline of the expert segmentation that were also correctly identified by our algorithm. We also compute the same percentage for a dilation of the automatic segmentation, by two and three voxels. We achieve a media 96% of the centerline voxels being within 3 voxels of a prediction, while we find a drop to 53% when comparing with the exact centerline. The latter drop is due to segmentations in the areas of the thick ganglia, where our method under-segments, and may follow a slightly off-center path.

= Conclusion =

As shown in the results, the proposed segmentation may slightly over-segment (usually by at most two voxels) in thin areas and under-segment in thick areas, but will give a very good estimation of the nerve core and location.

==Key Investigators==
* MIT: Adrian Dalca, Polina Golland
* BWH: Giovanna Danagoulian, Ehud Schmidt
* SPL: Ron Kikinis

Projects:NerveSegmentation

2011-03-31T21:40:15Z

Georgehchen: /* Description */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Nerve Segmentation =

Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bezier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking when compared to expert manual segmentation.

= Data =

[[File:NerveSegMREg.png|800px|thumb|center|We show three slices of an example High-Resolution MR scan. All arrows point to the same nerve bundle. Blue arrows show examples of poor contrast between the nerve and the surrounding tissue; orange arrows indicate the thickening of the neural tract into a ganglion.
]]

= Description =
We present a tracking approach based on particle filtering, also known as sequential Monte Carlo tracking. Tracking has also been used successfully for segmentation of tubular structures. Most vessel tracking methods model the state as a cross-sectional ellipse or as a cylindroid. In tracking nerve bundles, the regions of low contrast require the state to capture substantially longer segments of the track than what is represented by a cross-section. In addition, nerves tend to change direction, often sharply, which necessitates a use of more complex descriptors than cylinders.

To address the challenges of nerve tracking, we define a flexible particle representation that captures the geometric behavior of the nerve bundles. We use a Bezier spline centerline with a linear radius function to characterize a nerve bundle. We devise a dynamics model for particle updates that encourages continuity and smoothness. Furthermore, we define an image likelihood model that compares gradient fields and intensities of predicted patches with image observations to evaluate a posterior distribution of the particles' importance. Once tracking is completed, we remove spurious segmentations by measuring the quality of the entire tract. We demonstrate successful segmentations of neural tracts and evaluate them relative to expert manual segmentations. To the best of our knowledge, this is the first automatic segmentation of nerve bundles and ganglia.

= Results =

[[File:NerveSegResults.png|800px|thumb|center|
The above figure shows a summary of our results. On the left, we show a rendering of segmentation results -- the rightmost nerve shows results without post-processing pruning, while the left segmentation was processed after completion of tracking with an automatic post-processing step. On the right, we present two measures, precision and sensitivity, as box plots.]]

Here we compare our segmentations with expert delineations. We define precision as the percentage of voxels identified by the algorithm as nerve that were also marked as nerve by an expert. Precision coarsely quantifies our ability to separate the nerve from the surroundings. We also compute precision for a dilation of the manual segmentation by two and three voxels. The results for precision are shown in the left part of the graph, where we note that we reach a median $97.3\%$ of our proposed segmentation being within 2 voxels of the expert segmentation, while due to partial volume effect we over-segment the thin nerves and we only achieve a 68% precision with respect to exact segmentation.

Since the main goal of nerve tracking is to extract the full path of the nerve accurately, while it is less crucial to identify the bundle boundaries, we also define sensitivity to quantify the agreement between the automatically defined path and the centerline of the manual segmentation. We define sensitivity as the percentage of voxels in the centerline of the expert segmentation that were also correctly identified by our algorithm. We also compute the same percentage for a dilation of the automatic segmentation, by two and three voxels. We achieve a media 96% of the centerline voxels being within 3 voxels of a prediction, while we find a drop to 53% when comparing with the exact centerline. The latter drop is due to segmentations in the areas of the thick ganglia, where our method under-segments, and may follow a slightly off-center path.

= Conclusion =

As shown in the results, the proposed segmentation may slightly over-segment (usually by at most two voxels) in thin areas and under-segment in thick areas, but will give a very good estimation of the nerve core and location.

==Key Investigators==
* MIT: Adrian Dalca, Polina Golland
* BWH: Giovanna Danagoulian, Ehud Schmidt
* SPL: Ron Kikinis